CAViaR: Conditional autoregressive value at risk by regression quantiles. Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, we introduce a new test of model adequacy, the dynamic quantile test. Applications to real data provide empirical support to this methodology.

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  1. Ando, Tomohiro; Bai, Jushan: Quantile co-movement in financial markets: a panel quantile model with unobserved heterogeneity (2020)
  2. Meng, Xiaochun; Taylor, James W.: Estimating value-at-risk and expected shortfall using the intraday low and range data (2020)
  3. Nguyen, Giang; Engle, Robert; Fleming, Michael; Ghysels, Eric: Liquidity and volatility in the U.S. Treasury market (2020)
  4. Tay, Hao-Zhe; Ng, Kok-Haur; Koh, You-Beng; Ng, Kooi-Huat: Model selection based on value-at-risk backtesting approach for GARCH-type models (2020)
  5. Altun, Emrah: Two-sided exponential-geometric distribution: inference and volatility modeling (2019)
  6. Amédée-Manesme, Charles-Olivier; Barthélémy, Fabrice; Maillard, Didier: Computation of the corrected Cornish-Fisher expansion using the response surface methodology: application to \textitVaRand \textitCVaR (2019)
  7. Bu, Di; Liao, Yin; Shi, Jing; Peng, Hongfeng: Dynamic expected shortfall: a spectral decomposition of tail risk across time horizons (2019)
  8. Calabrese, Raffaella; Osmetti, Silvia Angela: A new approach to measure systemic risk: a bivariate copula model for dependent censored data (2019)
  9. Chen, Yu; Wang, Zhicheng; Zhang, Zhengjun: Mark to market value at risk (2019)
  10. Dimitriadis, Timo; Bayer, Sebastian: A joint quantile and expected shortfall regression framework (2019)
  11. Giessing, Alexander; He, Xuming: On the predictive risk in misspecified quantile regression (2019)
  12. Kim, Moosup; Lee, Sangyeol: Test for tail index constancy of GARCH innovations based on conditional volatility (2019)
  13. Novales, Alfonso; Garcia-Jorcano, Laura: Backtesting extreme value theory models of expected shortfall (2019)
  14. Patton, Andrew J.; Ziegel, Johanna F.; Chen, Rui: Dynamic semiparametric models for expected shortfall (and Value-at-Risk) (2019)
  15. Petrella, Lea; Raponi, Valentina: Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress (2019)
  16. Tian, Ding-shi; Cai, Zong-wu; Fang, Ying: Econometric modeling of risk measures: a selective review of the recent literature (2019)
  17. Wang, Chao; Chen, Qian; Gerlach, Richard: Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution (2019)
  18. Wu, Qi; Yan, Xing: Capturing deep tail risk via sequential learning of quantile dynamics (2019)
  19. Zhu, Xuening; Wang, Weining; Wang, Hansheng; Härdle, Wolfgang Karl: Network quantile autoregression (2019)
  20. Berger, Theo; Gençay, Ramazan: Improving daily value-at-risk forecasts: the relevance of short-run volatility for regulatory quality assessment (2018)

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